计算机化自适应诊断测验中原始题的属性标定

认知诊断测验项目开发成本较高, 要标定大量项目的属性相当费时费力, 专家完成这一任务也比较困难。对于在计算机化自适应诊断测验中的项目属性的标定尚未见到报导。在已有的为诊断测验开发的小型题库基础上, 本文在计算机化自适应认知诊断测验过程中, 植入原始题, 对项目属性标定的问题进行探讨, 重点研究原始题属性标定的方法及其影响因素, 除了MMLE方法和MLE方法外, 还建立了一种新的可用于所有非补偿认知诊断模型的属性标定的方法—— 交差方法。Monte Carlo模拟结果显示, MMLE方法较MLE方法好; 在知识状态估计精度较高时, 自适应植入原始题较随机植入原始题有一定的优势; 随着知识状态估计精度提高和原始题作答次数增加, 交差方法与MLE方法基本相当, 只是在发散型和无结构型表现欠佳, 但是交差方法不需要预先设定项目参数值。     

认知诊断计算机化自适应测验中的项目增补

项目的增补对认知诊断计算机化自适应测验(CD-CAT)题库的开发与维护至关重要。借鉴单维项目反应理论(IRT)中联合极大似然估计方法(JMLE)的思路, 提出联合估计算法(JEA), 仅依赖被试在旧题和新题上的作答反应联合地、自动地估计新题的属性向量和新题的项目参数。研究结果表明：当项目参数相对较小且样本量相对较大时, JEA算法在新题属性向量和新题项目参数估计精度方面表现不错; 而且样本大小、项目参数大小以及项目参数初值都影响着JEA算法的表现。     

基于属性掌握概率的认知诊断计算机化自适应测验选题策略

在认知诊断计算机化自适应测验(CD-CAT)中, 被试对每个属性的掌握概率更直接地反映了被试能力的当前估计值。因此, 基于被试的属性掌握概率来构建选题策略, 选择最能改变被试属性掌握概率的题目作为下一个测验项目, 这应该是一个值得尝试的方案。本文借鉴已有相关研究的数据生成模式进行探索, 模拟实验结果表明：假设属性间相互独立,在定长(长度为16)、变长(长度为16或后验属性掌握模式概率达到0.8)以及短测验(长度分别为4、6、8、10)的情况下, 基于属性掌握概率的选题策略PPWKL和PHKL有较好的分类准确率, 在题目曝光率, 题库使用均匀性等方面也有较好的表现; 与研究较多的PWKL、HKL等策略相比, 也略有优势; 当属性间存在不同程度的相关时, 在定长、变长以及较短的测验条件下, 基于PHKL和MI的测验对知识状态估计精度较好, 基于PPWKL和PHKL的测验综合表现占优。     

Logistic加权模型的理论构建与模拟分析

试题难度、试题考查重要性程度加权是多级记分试题的两个基本属性, 因而在IRT项目特征函数中需用不同参数来表示。以往多级记分模型用多个难度参数来描述多级记分试题的难度, 不能有效的表达多级记分试题的分数权重作用。从多级记分试题的分数加权作用角度, 本文提出Logistic加权模型并论述了理论构建思想。在Logistic加权模型下对项目参数估计的EM算法进行推导并编写了相应的参数估计程序。在Logistic加权模型下进行测验模拟, 发现项目参数估计的模拟返真性能良好。     

Are highly numerate individuals invulnerable to attribute framing bias? Comparing numerically and graphically represented attribute framing

Judgments and decisions are frequently biased by attribute framing, presenting either positive or negative attributes of an object. This paper focused on two factors previously shown to moderate the attribute‐framing bias: mode of presentation and participants' numeric ability. Whereas many studies demonstrated that graphical display reduced the bias, recent findings suggest that graphical manipulation can nevertheless elicit significant framing bias. Numeracy has been shown to moderate attribute‐framing bias when the quantitative information was represented by numbers. The present study examined to what extent numeracy would still moderate the framing bias when it is graphically elicited. The results showed a significant framing bias for graphically as well as for numerically represented framing scenarios. Critically, whereas numeracy moderated the framing bias in numerically represented scenarios, it did not have a similar moderating effect when the quantitative information in the scenario was graphically represented. Theoretical and practical implications of the findings are discussed.     

小学四、五年级数学诊断性测验的编制——基于规则空间模型的方法

基于规则空间模型, 以小学四、五年级数学诊断性测验的编制为例, 探索了认知诊断理论背景下诊断性测验的编制方法。研究发现, 基于规则空间模型编制的诊断性测验具备优良的信效度, 尤其在结构效度上具有突出优势。应用该测验对1059名四、五年级学生进行诊断测验的结果显示:在整体上, 学生对整数、初级运算与应用掌握得较为巩固, 对量、统计、规律、高级运算掌握较差;在发展趋势上, 量、统计、规律、高级运算是四、五年级之间进步最快的属性。     

A Person Fit Test For Irt Models For Polytomous Items

A person fit test based on the Lagrange multiplier test is presented for three item response theory models for polytomous items: the generalized partial credit model, the sequential model, and the graded response model. The test can also be used in the framework of multidimensional ability parameters. It is shown that the Lagrange multiplier statistic can take both the effects of estimation of the item parameters and the estimation of the person parameters into account. The Lagrange multiplier statistic has an asymptotic χ²-distribution. The Type I error rate and power are investigated using simulation studies. Results show that test statistics that ignore the effects of estimation of the persons’ ability parameters have decreased Type I error rates and power. Incorporating a correction to account for the effects of the estimation of the persons’ ability parameters results in acceptable Type I error rates and power characteristics; incorporating a correction for the estimation of the item parameters has very little additional effect. It is investigated to what extent the three models give comparable results, both in the simulation studies and in an example using data from the NEO Personality Inventory-Revised.     

Using Ordinal Data to Estimate Cardinal Values

We investigate methods developed in multiple criteria decision‐making that use ordinal information to estimate numerical values. Such methods can be used to estimate attribute weights, attribute values, or event probabilities given ranks or partial ranks. We first review related studies and then develop a generalized rank‐sum (GRS) approach in which we provide a derivation of the rank‐sum approach that had been previously proposed. The GRS approach allows for incorporating the concept of degree of importance (or, difference in likelihood with respect to probabilities and difference in value for attribute values), information that most other rank‐based formulas do not utilize. We then present simulation results comparing the GRS method with other rank‐based formulas such as the rank order centroid method and comparing the GRS methods using as many as three levels of importance (i.e., GRS‐3) with Simos' procedure (which can also incorporate degree of importance). To our surprise, our results show that the incorporation of additional information (i.e., the degree of the importance), both GRS‐3 and Simos' procedure, did not result in better performance than rank order centroid or GRS. Further research is needed to investigate the modelling of such extra information. We also explore the scenario when a decision‐maker has indifference judgments and cannot provide a complete rank order. Copyright © 2014 John Wiley & Sons, Ltd.     

The role of structural consistency between categories and attributes in hierarchical category learning

This study investigated how consistency between categories and attributes determines attribute selection in hierarchical category learning. Participants learned six categories for which number and color were equally relevant attributes, followed by a transfer task, to test which attribute was used. Before that, half of them learned embedding higher-level categories for which numbers were likely to be used. Orthogonal to this factor, the hierarchical structure was made explicit for half of them by category labels. The results showed that participants used numbers in the prior learning, but that the use of numbers was inhibited in the subsequent six-category learning task. However, this inhibitory effect was reduced when the hierarchical structure was explicit. The pattern of results suggests that attribute selection is determined by structural consistency between categories and attributes, not by a prior use of an attribute.